I provide my thoughts and main takeaways for active learning.
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Submitted to Inverse Problems.
We’re awaiting review from funding to post and submit this paper. Will be submitting to SIAM Mathematics of Data Science (SIMODS).
Published in IEEE ICASSP, 2017
Published in ICML Workshop on Real World Experiment Design and Active Learning, 2020
Presented summer testing and research from my internship at LLNL, sorry poster not available.
Won prize for best presentation of my session of the BYU Spring Research Conference. Presented research on estimating the latent number of clusters in directed networks for use in spectral clustering. The sequence of smallest eigenvalues of the associated graph Laplacian matrix that are real turned out to be a good estimator for the latent clustering structure.
Won prize for best presentation of my session of the BYU Spring Research Conference. Presented research on using effective resistance for use in link prediction by viewing link prediction as a probabilistic problem wherein we view the current graph’s edge set as a realization of draws from an underlying probability distribution determined by a ground truth graph’s effective resistances.
SIAM PDE 2019, Minisymposium on PDEs in Machine Learning. Presented results on consistency of semi-supervised regression in graph learning, from our paper.
SIAM CSE 2021, Minisymposium on Theory and Applications of Graph-Based Learning. Presented my work on model change active learning for graph-based semi-supervised learning (SSL), where we use the approximate change in the underlying SSL model as a measure of usefulness in the active learning process. This approximate change is efficiently done for a family of graph-based SSL models, using only a subset of the graph Laplacian’s eigenvalues and eigenvectors.
Guest Lecturer for Dr. Jared Whitehead’s Mathematics of Machine Learning graduate level course at BYU. Presented my work on model change active learning for graph-based semi-supervised learning (SSL) as well as gave some advice on applying for Ph.D. programs in Applied Mathematics.
Machine Learning Consulting, University of California, Los Angeles, 2020
I’m open to help with any ideas or questions you have relating to machine learning applications.
Undergraduate tutoring, University of California, Los Angeles, 2020
Looking for help with your undergraduate courses at UCLA (or other schools/universities)? I’m happy to help.